Accepted Papers

Easy Perturbation EEG Algorithm for Spectral Importance (easyPEASI): A simple method to identify important spectral features of EEG in deep learning models

David Nahmias: FDA UMD; Kimberly Kontson: U.S. FDA


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Efforts into understanding neurological differences between populations is an active area of research. Deep learning has recently shown promising results using EEG as input to distinguish recordings of subjects based on neurological activity. However, only about one quarter of these studies investigate the underlying neurophysiological implications. This work proposes and validates a method to investigate frequency bands important to EEG-driven deep learning models. Easy perturbation EEG algorithm for spectral importance (easyPEASI) is simpler than previous methods and requires only perturbations to input data. We validate easyPEASI on EEG pathology classification using the Temple University Health EEG Corpus. easyPEASI is further applied to characterize the effects of patients’ medications on brain rhythms. We investigate classifications of patients taking one of two anticonvulsant medications, Dilantin (phenytoin) and Keppra (levetiracetam), and subjects taking no medications. We find that for recordings of subjects with clinically-determined normal EEG that these medications effect the Theta and Alpha band most significantly. For recordings with clinically-determined abnormal EEG these medications affected the Delta, Theta, and Alpha bands most significantly. We also find the Beta band to be affected differently by the two medications. Results found here show promise for a method of obtaining explainable artificial intelligence and interpretable models from EEG-driven deep learning through a simpler more accessible method perturbing only input data. Overall, this work provides a fast, easy, and reproducible method to automatically determine salient spectral features of neural activity that have been learned by machine learning models, such as deep learning.

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